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Scientific, Technological and Production Journal
ISSN 2079–3332 (print)
ISSN 3034-6835 (online)
founded in 2001

Methodology for assessing the mining conditions of quarrying to find reserves to increase the intensity of mining operations

Original articles: 2025 No 4

Methodology for assessing the mining conditions of quarrying to find reserves to increase the intensity of mining operations

DOI: 10.56195/20793332-2025-25-4-23-30

Read in pdf V. D. Kantemirov, A. M. Yakovlev, R. S. Titov
  1. Institute of Mining, Ural Branch of the Russian Academy of Sciences, Yekaterinburg, Russian Federation
p. 23-30, No.4/2025
Abstract:
The article presents the basics of the developed methodology, which makes it possible to automatically assess the ratio of mining fronts and qualitative indicators of mineral reserves (PI) on the working horizons of the quarry. The proposed methodology is designed for automated planning of mining operations in the raw material quality management mode and allows you to determine the reserves of mining production; to establish the predominant technological grade of ore raw materials in the analyzed areas of the deposit; choose a rational way to extract raw materials, taking into account their quality. The use of the methodology helps to increase the efficiency of mining planning, taking into account the quality of raw materials, which in turn makes it possible to increase the degree of extraction of valuable components and ensure the integrated use of mineral resources.
Keywords:
mining intensification, mining front, excavator fleet, mining and geological information system, mining and geometric analysis, geometrization
For citation:
Kantemirov VD, Yakovlev AM, Titov RS. Methodology for assessing the mining conditions of quarrying to find reserves to increase the intensity of mining operations. Mine Surveying and Subsurface Use. 2025;25(4):23-30. (In Russ.). https://doi. org/10.56195/20793332-2025-25-4-23-30.
Information about the authors:
  1. Valery D. Kantemirov – Cand. Sci. (Eng.), Quality management laboratory chief, Institute of Mining, Ural Branch of the Russian Academy of Sciences, 620075, Yekaterinburg, Russian Federation, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it., https://orcid.org/0000-0001-6486-2740
  2. Andrei M. Yakovlev – Cand. Sci. (Eng.), Senior Researcher, Quality management laboratory, Institute of Mining, Ural Branch of the Russian Academy of Sciences, 620075, Yekaterinburg, Russian Federation, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it., https://orcid.org/0000-0001-8285-6387
  3. Roman S. Titov – Senior Researcher, Quality management laboratory, Institute of Mining, Ural Branch of the Russian Academy of Sciences, 620075, Yekaterinburg, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it., https://orcid.org/0000-0002-3569-2743
References:
  1. 1. Быховский ЛЗ, Тигунов ЛП. Стратегическое минеральное сырье: пути решения проблемы дефицита. Минеральные ресурсы России. Экономика и управление. 2015; 5: 43-49. [Bykhovsky LZ, Tigunov LP. Strategic mineral raw materials: ways to solve the shortage problem. Mineral Resources of Russia. Economics and management. 2015; 5: 43-49. (In Russ.)].
  2. 2. Арсентьев АИ. Производительность карьеров. Санкт-Петербург, 2002: 85. [Arsentiev AI. Productivity of quarries. St. Petersburg, 2002:85 (In Russ.)].
  3. 3. Моссаковский ЯВ. Экономика горной промышленности. Москва, 2015: 525. [Mossakovsky YaV. Economics of the mining industrу. Moscow, 2015: 525. (In Russ.)].
  4. 4. Кантемиров ВД. Технологические особенности освоения новых сырьевых баз. Горный информационно-аналитический бюллетень. 2014; 6: 369-373. [Kantemirov VD. Technological features of the development of new raw material bases. Mining information and analytical bulletin. 2014; 6: 369-373. (In Russ.)].
  5. 5. Debashish D, Abhiram KV. Fundamentals and Applications of Rock Mechanics. NewDelhi: PHILearning, 2016: 412.
  6. 6. Кузнецов ОЛ, Никитин АА, Черемисина ЕН. Геоинформатика и геоинформационные системы. Москва, 2005: 453. [Kuznecov OL, Nikitin AA, Cheremisinu EN. Geoinformatics and geoformation sistems. Mosow, 2005: 453 (In Russ.)].
  7. 7. Кантемиров ВД, Яковлев АМ, Титов РС. Оценка качественных показателей полезных ископаемых с использованием геоинформацион- ных технологий блочного моделирования. Геоинформатика. 2020; 3: 29-37. [Kantemirov VD, Yakovlev AM, Titov RS Evaluation of qualitative indicators of minerals using geoinformation technologies of block modeling. Geoinformatics. 2020; 3: 29-37. (In Russ.)].
  8. 8. Ларионова ОБ, Пилюгина ТП. Особенности компьютерной графики. Альманах современной науки и образования. 2008; 1: 116-118. [Larionova OB, Pilyugina TP. Features of computer graphics. Almanac of modern science and education. 2008; 1: 116-118. (In Russ.)].
  9. 9. Демьянов ВВ, Савельева ЕА. Геостатистика: теория и практика. Москва, 2010: 327. [Demyanov VV, Saveleva EA. Geostatistika: teoriyaipraktika. Moskow, 2010: 327. (In Russ.)].
  10. 10. Mohammadpour M, Bahroudi A, Abedi M., et al. Geochemical distribution mapping by combining number-size multifractal model and multiple indicator kriging. Journal of Geochemical Exploration. 2019; 200: 13-26.
  11. 11. Яковлев АМ. Апробация алгоритмов автоматизированной обработки геологических баз данных в технологических схемах управле- ния качеством минерального сырья. Горный информационно-аналитический бюллетень (научно-технический журнал). 2021; 5-1: 248-257. [Yakovlev AM. Approbation of algorithms for automated processing of geological databases in technological schemes for quality management of mineral raw materials. Mining Information and Analytical Bulletin (scientific and technical journal). 2021; 5-1: 248-257. (In Russ.)].
  12. 12. Илимбетов АФ, Рыльникова МВ, Радченко ДН и др. Новые решения проблемы комплексного освоения рудных месторождений. Вестник МГТУ им. Г.И. Носова. 2006; 4 (16): 8-13. [Ilimbetov AF, Rylnikova MV, Radchenko DN, et al. New solutions to the problem of integrated development of ore deposits. Bulletin of the Moscow State Technical University named after G.I. Nosov. 2006; 4 (16): 8-13. (In Russ.)].
  13. 13. Asad M. W. A. Cutoff grade optimization algorithm with stockpiling option for open pit mining operations of two economic minerals. International Journal of Surface Mining, Reclamation and Environment. 2005; 19 (3): 176-187.
  14. 14. Clementini E, Di Felice P, van Oosterom P. A small set of formal topological relationships suitable for end-user interaction. Advances in Spatial Databases: Third International Symposium SSD ‘93 Singapore, June 23-25. Singapore, 1993.
  15. 15. Frank U. Multi-perspective enterprise modeling: foundational concepts, prospects and future research challenges. Software & Systems Modeling. 2014; 13 (3): 941-962.
  16. 16. Marques DM, Joao F. C. Choosing a proper sampling interval for the ore feeding a processing plant: A geostatistical solutio. International Journal of Mineral Processing. 2014; 131: 31-42.
  17. 17. Afzal P, Madani N, Shahbeik S, et al. Multi-Gaussian kriging: a practice to enhance delineation of mineralized zones by Concentration – Volume fractal model in Dardevey iron ore deposit, SE Iran. Journal of Geochemical Exploration. 2015; 158: 10-21.
  18. 18. Mery N, Emery X, Caceres A, et al. Geostatistical modeling of the geological uncertainty in an iron ore deposit. Ore Geology Reviews. 2017; 88: 336-351.

 

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